The popularity of digital music has made it possible for the average music listener to amass a large number of songs on their computer, portable media player, or other device. Digital music has also led to the introduction of online digital music services that make vast libraries of songs available to the listener at the click of a button. Although the increased availability of music is generally beneficial, the sheer number of song choices available to the listener can be overwhelming, which can lead to the listener only listening to a small subset of the available music.
Creating a playlist comprising a random list of a listener's music collection is one method that has been used in the prior art to expose the listener to a wider array of songs. Unfortunately, a playlist based on a simple randomization of the songs in a music library typically results in poor song sequencing, in that a subsequent song may be selected from a different genre, tempo, or the like. For example, randomization may place a heavy metal song directly after a classical piano song, leading to a less than desirable listening experience.
Although listeners enjoy much of the music in their personal libraries, they frequently also like to hear music from their favorite artists, or music that is similar in style to their favorite or preferred music. In some instances, listener-preferred music types (e.g., genre, tempo, artists, etc.) may change depending on the listener's environment. By way of example, without limitation, a listener may prefer classical music to be played while the listener is at his or her job; news, political and/or sports talk programs while driving during the week; heavy metal while at the gym; and a combination of bluegrass and zydeco while driving on the weekend.